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d80d020c86
update_position runs per tracked object per frame. While a position has fewer than 10 samples it calls np.percentile four times, and average_boxes (per stationary object per frame) calls np.mean four times - all on lists of at most 10 ints, where numpy's per-call dispatch/validation overhead dominates the actual work. Replace them with pure-Python equivalents: - average_boxes: sum()/len() instead of np.mean (bit-identical output) - interpolated_percentile(): linear-interpolated percentile matching numpy.percentile (including its lerp branch at frac>=0.5) for the small lists used here, in place of np.percentile Measured in the release image (numpy 1.26.4) on a 10-element list: np.percentile 18735 ns -> 191 ns/call (98x); np.mean-based average_boxes 7480 ns -> 591 ns (12.7x); ~74 us saved per object-frame in update_position. A live py-spy --gil profile of a camera process_frames worker showed np.percentile (update_position) and np.mean (average_boxes) among the top Frigate-owned on-CPU frames. Output is unchanged: added tests assert both helpers are bit-identical to numpy over randomized small inputs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .. | ||
| api | ||
| camera | ||
| comms | ||
| config | ||
| data_processing | ||
| db | ||
| detectors | ||
| embeddings | ||
| events | ||
| genai | ||
| images | ||
| jobs | ||
| motion | ||
| object_detection | ||
| output | ||
| ptz | ||
| record | ||
| review | ||
| service_manager | ||
| stats | ||
| test | ||
| track | ||
| util | ||
| video | ||
| __init__.py | ||
| __main__.py | ||
| app.py | ||
| const.py | ||
| debug_replay.py | ||
| ffmpeg_presets.py | ||
| log.py | ||
| models.py | ||
| mypy.ini | ||
| plus.py | ||
| storage.py | ||
| timeline.py | ||
| types.py | ||
| watchdog.py | ||